Case study
How we designed AI to reduce teacher workload without losing curriculum control
Validating an AI-assisted learning platform that combined curriculum-aligned generation, human approval, and scalable school workflows.
- Education
- EdTech
- AI-assisted learning
- Product Discovery & Validation
- AI feasibility assessment
- AI workflow design
- Responsible AI governance
- Client / Initiative
- AIQ Nexus
- Industry / Domain
- Education, EdTech, AI-assisted learning
- LLI role
- Product strategy, feasibility validation, UX architecture, PoC definition
- Scope
- AI-enabled learning platform for physics education, system workflows, approval model, and delivery framework
Overview
Reducing teacher overload by turning AI into a trusted, curriculum-aligned teaching assistant.
The problem
Physics teachers spend a disproportionate amount of time on repetitive, low-value tasks such as lesson preparation, test creation, timetable alignment, and identifying student misconceptions.
- Teaching quality depended heavily on individual effort rather than scalable support.
- Curriculum-aligned content creation was manual and time-consuming.
- School administrators lacked real insight into curriculum execution and teacher workload.
- Introducing AI without proper governance risked loss of trust, quality, and educational consistency.
This problem affected
- 01
Teachers, who had limited time for real teaching and growth.
- 02
School administrators, who lacked visibility and control.
- 03
Students, whose learning quality varied depending on teachers' capacity.
- 04
The organisation, which needed a scalable and responsible AI-first model.
This was a structural and systemic problem, not a tooling gap.
What was at stake
- Risk of AI-generated content being incorrect, inconsistent, or misaligned with curriculum standards.
- Bottlenecks caused by the limited availability of subject matter experts.
- Low adoption if teachers did not trust AI-generated materials.
- High operational cost if AI required constant manual supervision.
A poorly designed solution would have created more work, not less.
Why LLI
LLI was brought in to validate whether this idea was:
Technically feasible.
Educationally responsible.
Scalable without sacrificing quality.
The client needed a partner who could:
- Design AI workflows with human oversight.
- Balance automation with expert approval.
- Think beyond a demo and into real-world school operations.
Partial development or isolated AI experiments were not sufficient. This required end-to-end product thinking.
Our approach
We focused on decision-making before execution.
Guiding principles
- 01
AI should support teachers, not replace them.
- 02
Subject matter experts are a governance layer, not content factories.
- 03
Curriculum stability and change needed to be addressed upfront.
- 04
The system must work even with limited expert availability.
Key trade-offs
Introducing an approval queue instead of full automation.
Pre-generating curriculum content to reduce SME bottlenecks.
Designing distinct workflows for super admins, school admins, and teachers.
We prioritised trust, scalability, and adoption over raw AI capability.
The solution
We defined and validated a platform concept with clearly defined roles, workflows, and AI boundaries.
AI-assisted curriculum and lesson generation
- Daily lesson hooks.
- Teacher and student tests.
- Misconception detection.
- Knowledge boards and learning materials.
Role-based system architecture
- Super Admin (Physics SME) for approvals and quality control.
- School Administrator for curriculum and timetable management.
- Teachers as end users focused on teaching, not administration.
AI approval and governance model
- Central approval queue to prevent uncontrolled AI output.
- Recommendation to pre-create curriculum-wide content and allow selective approval.
Key design choices
Clear separation of responsibilities across roles.
AI treated as a generator, not an authority.
Mobile and web-friendly dashboards for daily usage.
Early identification of SME availability as a scaling constraint.
How the solution addressed the problem
- 01
Reduced repetitive work for teachers.
- 02
Maintained curriculum integrity.
- 03
Enabled controlled AI adoption in schools.
- 04
Created a foundation for expansion beyond physics.
Technology stack
Defined at the PoC level to support scalability and flexibility:
Backend
Ruby on Rails
Frontend
Vue.js
Administration
Active Admin
AI models
Claude or ChatGPT
AI architecture
RAG / CAG
These choices supported rapid validation, governance, and future extensibility.
Claude and ChatGPT were suitable commercial model options considered at the PoC stage, not technologies definitively selected for production.
Dedicated team
Full Stack Engineers
Frontend and Backend Engineers
AI Engineer
QA Engineer
DevOps
UI/UX Designer
Delivery Manager
Team structure: 6 engineers plus design and delivery leadership.
Engineering standards
AI outputs gated by human approval.
Clear system workflows documented upfront.
Early risk identification around SME bottlenecks.
Architecture designed for curriculum changes and scale.
Results and impact
For teachers
- Less time spent on preparation.
- More focus on teaching and student interaction.
- Access to structured, AI-supported materials.
For schools
- Better curriculum consistency.
- Improved visibility into teaching processes.
- Scalable support without increasing staff load.
For the organisation
- A validated AI-first product concept.
- Clear delivery roadmap and timeline.
- Reduced risk before full investment.
Why this case matters
This case shows how LLI approaches AI projects.
We start by defining where responsibility sits, how AI fits into the real workflow, and what needs to be validated before further investment.
For AIQ Nexus, that meant designing the product around curriculum integrity, teacher trust, human approval, and the practical availability of subject matter experts.
The result was a validated product concept in which AI could support teachers at scale while keeping educational quality and oversight within clearly defined boundaries.
The value: AIQ Nexus gained a validated product concept, governance model, and delivery framework for turning AI into a scalable teaching assistant without giving up human oversight or curriculum control.
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